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Record W4401506366 · doi:10.1136/bmjopen-2023-083783

Case management in primary healthcare for people with complex needs to improve integrated care: a large-scale implementation study protocol

2024· article· en· W4401506366 on OpenAlexafffundabout
Catherine Hudon, Maud‐Christine Chouinard, Shelley Doucet, Helena Piccinini‐Vallis, Kimberly Fairman, Tara Sampalli, Joanna Zed, Magaly Brodeur, Denis Chênevert, Andréa Dépelteau, Mariève Dupont, Marlène Karam, France Légaré, Alison Luke, Marilyn Macdonald, Adèle Morvannou, Vivian R. Ramsden, Lourdes Rodríguez del Barrio, Sabrina T. Wong, Mireille Lambert, Mathieu Bisson, Charlotte Schwarz, René Benoit, Marie-Dominique Poirier, Audrey-Lise Rock-Hervieux, Donna Rubenstein, Linda Wilhelm

Bibliographic record

VenueBMJ Open · 2024
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsAluminium Refining, Degassing and Filtering (Canada)HEC MontréalNova Scotia Health AuthorityUniversity of British ColumbiaInstitute for Circumpolar Health ResearchDalhousie UniversityUniversity of New BrunswickUniversity of SaskatchewanUniversité LavalUniversité de MontréalUniversité de Sherbrooke
FundersCanadian Institutes of Health Research
KeywordsMedicineProtocol (science)Primary careScale (ratio)Health careHealth services researchPrimary health careNursingPublic healthFamily medicineAlternative medicineEnvironmental healthPathologyPopulation

Abstract

fetched live from OpenAlex

INTRODUCTION: Case management (CM) is among the most studied effective models of integrated care for people with complex needs. The goal of this study is to scale up and assess CM in primary healthcare for people with complex needs. METHODS AND ANALYSIS: The research questions are: (1) which mechanisms contribute to the successful scale-up of CM for people with complex needs in primary healthcare?; (2) how do contextual factors within primary healthcare organisations contribute to these mechanisms? and (3) what are the relationships between the actors, contextual factors, mechanisms and outcomes when scaling-up CM for people with complex needs in primary healthcare? We will conduct a mixed methods Canadian interprovincial project in Quebec, New-Brunswick and Nova Scotia. It will include a scale-up phase and an evaluation phase. At inception, a scale-up committee will be formed in each province to oversee the scale-up phase. We will assess scale-up using a realist evaluation guided by the RAMESES checklist to develop an initial programme theory on CM scale-up. Then we will test and refine the programme theory using a mixed-methods multiple case study with 10 cases, each case being the scalable unit of the intervention in a region. Each primary care clinic within the case will recruit 30 adult patients with complex needs who frequently use healthcare services. Qualitative data will be used to identify contexts, mechanisms and certain outcomes for developing context-mechanism-outcome configurations. Quantitative data will be used to describe patient characteristics and measure scale-up outcomes. ETHICS AND DISSEMINATION: Ethics approval was obtained. Engaging researchers, decision-makers, clinicians and patient partners on the study Steering Committee will foster knowledge mobilisation and impact. The dissemination plan will be developed with the Steering Committee with messages and dissemination methods targeted for each audience.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.103
metaresearch head score (Gemma)0.065
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.103
Threshold uncertainty score0.545

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1030.065
Meta-epidemiology (narrow)0.0030.005
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0060.005
Science and technology studies0.0090.004
Scholarly communication0.0050.005
Open science0.0070.006
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0380.008

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.096
GPT teacher head0.565
Teacher spread0.469 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes3
Has abstractyes

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